{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n",
      "sys.version_info(major=3, minor=7, micro=5, releaselevel='final', serial=0)\n",
      "numpy 1.16.4\n",
      "pandas 0.25.3\n",
      "sklearn 0.22\n",
      "tensorflow 2.0.0\n",
      "tensorflow_core.keras 2.2.4-tf\n",
      "Test Images ->  148\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import time\n",
    "import random\n",
    "import pathlib\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "\n",
    "\n",
    "test_path = pathlib.Path('./lol_data_1/test')\n",
    "\n",
    "test_image_paths = list(test_path.glob('*/'))  \n",
    "\n",
    "test_image_paths = [str(path) for path in test_image_paths]\n",
    "\n",
    "\n",
    "random.shuffle(test_image_paths)\n",
    "\n",
    "\n",
    "test_image_count = len(test_image_paths)\n",
    "\n",
    "print(\"Test Images -> \", test_image_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lol_data_1\\\\test\\\\Fiddlesticks.jpg',\n",
       " 'lol_data_1\\\\test\\\\Trundle.jpg',\n",
       " 'lol_data_1\\\\test\\\\Quinn.jpg',\n",
       " 'lol_data_1\\\\test\\\\Pyke.jpg',\n",
       " 'lol_data_1\\\\test\\\\Graves.jpg']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lol_data_1\\test\\Fiddlesticks.jpg  --->   Fiddlesticks\n",
      "lol_data_1\\test\\Trundle.jpg  --->   Trundle\n",
      "lol_data_1\\test\\Quinn.jpg  --->   Quinn\n",
      "lol_data_1\\test\\Pyke.jpg  --->   Pyke\n",
      "lol_data_1\\test\\Graves.jpg  --->   Graves\n"
     ]
    }
   ],
   "source": [
    "test_image_labels = [path.split('\\\\')[2].split('.')[0] for path in test_image_paths]\n",
    "for image, label in zip(test_image_paths[:5], test_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels_info = []\n",
    "for image_path, label in zip(test_image_paths, test_image_labels):\n",
    "    test_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           filepath         class\n",
      "0  lol_data_1\\test\\Fiddlesticks.jpg  Fiddlesticks\n",
      "1       lol_data_1\\test\\Trundle.jpg       Trundle\n",
      "2         lol_data_1\\test\\Quinn.jpg         Quinn\n",
      "3          lol_data_1\\test\\Pyke.jpg          Pyke\n",
      "4        lol_data_1\\test\\Graves.jpg        Graves\n"
     ]
    }
   ],
   "source": [
    "test_df = pd.DataFrame(test_labels_info)\n",
    "\n",
    "test_df.columns = ['filepath', 'class']\n",
    "\n",
    "print(test_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('./lol-callbacks/face_recognition_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 103748 validated image filenames belonging to 148 classes.\n",
      "Found 26640 validated image filenames belonging to 148 classes.\n",
      "Found 147 validated image filenames belonging to 148 classes.\n"
     ]
    }
   ],
   "source": [
    "height = 64\n",
    "width = 64\n",
    "channels = 3\n",
    "batch_size = 128\n",
    "num_classes = 148\n",
    "\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    #像素值 都除以255\n",
    "    rescale = 1./255,\n",
    "    # 图片随机旋转 (5度以内)\n",
    "    rotation_range = 20,\n",
    "    # 图片左右位移  20%限度以内\n",
    "    width_shift_range = 0.2,\n",
    "    # 图片上下位移  20%限度以内\n",
    "    height_shift_range = 0.2,\n",
    "    # 图像剪切强度\n",
    "    shear_range = 0.2,\n",
    "    # 图像缩放强度\n",
    "    zoom_range = 0.2,\n",
    "    # 是否水平翻转\n",
    "    horizontal_flip = False,\n",
    "    # 放大缩小吼， 像素填充方式\n",
    "    fill_mode = 'nearest',\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(train_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 2333,\n",
    "                                                    shuffle = True,\n",
    "                                                    class_mode = \"categorical\")\n",
    "\n",
    "valid_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    ")\n",
    "valid_generator = valid_datagen.flow_from_dataframe(valid_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 666,\n",
    "                                                    shuffle = True,\n",
    "                                                    class_mode = \"categorical\")\n",
    "\n",
    "test_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    ")\n",
    "test_generator = test_datagen.flow_from_dataframe(test_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 9727,\n",
    "                                                    shuffle = True,\n",
    "                                                    class_mode = \"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Generator Sample ->  103748\n",
      "Validation Generator Sample ->  26640\n",
      "Test Generator Sample ->  147\n"
     ]
    }
   ],
   "source": [
    "train_num = train_generator.samples\n",
    "valid_num = valid_generator.samples\n",
    "test_num = test_generator.samples\n",
    "\n",
    "print(\"Training Generator Sample -> \", train_num)\n",
    "print(\"Validation Generator Sample -> \", valid_num)\n",
    "print(\"Test Generator Sample -> \", test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 64, 64, 3) (64, 148)\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "(64, 64, 64, 3) (64, 148)\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    x, y = train_generator.next()\n",
    "    print(x.shape, y.shape)\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 64, 64, 16)        448       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "alpha_dropout (AlphaDropout) (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 148)               19092     \n",
      "=================================================================\n",
      "Total params: 615,588\n",
      "Trainable params: 615,588\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    \n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(128, activation = 'selu'),\n",
    "    keras.layers.AlphaDropout(rate=0.5),\n",
    "    \n",
    "    keras.layers.Dense(num_classes, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "             optimizer = \"adam\", metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = './lol-callbacks'\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)\n",
    "\n",
    "output_model_file = os.path.join(logdir, \"face_recognition_model.h5\")\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(output_model_file, save_best_only=True),\n",
    "    keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1621/1621 [==============================] - 503s 310ms/step - loss: 1.2871 - accuracy: 0.6726 - val_loss: 0.6152 - val_accuracy: 0.9082\n",
      "Epoch 2/20\n",
      "1621/1621 [==============================] - 372s 229ms/step - loss: 0.4199 - accuracy: 0.8781 - val_loss: 0.2512 - val_accuracy: 0.9541\n",
      "Epoch 3/20\n",
      "1621/1621 [==============================] - 190s 117ms/step - loss: 0.3191 - accuracy: 0.9081 - val_loss: 0.2824 - val_accuracy: 0.9559\n",
      "Epoch 4/20\n",
      "1621/1621 [==============================] - 191s 118ms/step - loss: 0.2808 - accuracy: 0.9221 - val_loss: 0.0569 - val_accuracy: 0.9881\n",
      "Epoch 5/20\n",
      "1621/1621 [==============================] - 195s 120ms/step - loss: 0.2651 - accuracy: 0.9292 - val_loss: 0.0849 - val_accuracy: 0.9892\n",
      "Epoch 6/20\n",
      "1621/1621 [==============================] - 197s 121ms/step - loss: 0.2524 - accuracy: 0.9348 - val_loss: 0.0618 - val_accuracy: 0.9912\n",
      "Epoch 7/20\n",
      "1621/1621 [==============================] - 190s 117ms/step - loss: 0.2374 - accuracy: 0.9402 - val_loss: 0.2136 - val_accuracy: 0.9740\n",
      "Epoch 8/20\n",
      "1621/1621 [==============================] - 188s 116ms/step - loss: 0.2588 - accuracy: 0.9399 - val_loss: 0.0404 - val_accuracy: 0.9935\n",
      "Epoch 9/20\n",
      "1621/1621 [==============================] - 193s 119ms/step - loss: 0.2324 - accuracy: 0.9466 - val_loss: 0.1379 - val_accuracy: 0.9845\n",
      "Epoch 10/20\n",
      "1621/1621 [==============================] - 191s 118ms/step - loss: 0.2525 - accuracy: 0.9447 - val_loss: 0.0142 - val_accuracy: 0.9976\n",
      "Epoch 11/20\n",
      "1621/1621 [==============================] - 179s 110ms/step - loss: 0.2497 - accuracy: 0.9483 - val_loss: 0.0272 - val_accuracy: 0.9959\n",
      "Epoch 12/20\n",
      "1621/1621 [==============================] - 180s 111ms/step - loss: 0.2494 - accuracy: 0.9502 - val_loss: 0.2849 - val_accuracy: 0.9740\n",
      "Epoch 13/20\n",
      "1621/1621 [==============================] - 178s 110ms/step - loss: 0.2695 - accuracy: 0.9497 - val_loss: 0.0154 - val_accuracy: 0.9982\n",
      "Epoch 14/20\n",
      "1621/1621 [==============================] - 181s 112ms/step - loss: 0.2703 - accuracy: 0.9528 - val_loss: 0.0601 - val_accuracy: 0.9935\n",
      "Epoch 15/20\n",
      "1621/1621 [==============================] - 177s 109ms/step - loss: 0.2776 - accuracy: 0.9537 - val_loss: 0.0766 - val_accuracy: 0.9931\n",
      "Epoch 16/20\n",
      "1621/1621 [==============================] - 178s 110ms/step - loss: 0.2946 - accuracy: 0.9533 - val_loss: 0.1186 - val_accuracy: 0.9891\n",
      "Epoch 17/20\n",
      "1621/1621 [==============================] - 178s 110ms/step - loss: 0.3003 - accuracy: 0.9549 - val_loss: 0.0211 - val_accuracy: 0.9978\n",
      "Epoch 18/20\n",
      "1621/1621 [==============================] - 181s 111ms/step - loss: 0.3090 - accuracy: 0.9550 - val_loss: 0.1189 - val_accuracy: 0.9888\n",
      "Epoch 19/20\n",
      "1621/1621 [==============================] - 178s 110ms/step - loss: 0.3321 - accuracy: 0.9572 - val_loss: 0.0788 - val_accuracy: 0.9926\n",
      "Epoch 20/20\n",
      "1621/1621 [==============================] - 179s 110ms/step - loss: 0.3316 - accuracy: 0.9569 - val_loss: 0.0200 - val_accuracy: 0.9981\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "#因为数据是generator 产生的 所以不能用fit函数\n",
    "history = model.fit_generator(train_generator, steps_per_epoch=train_num // batch_size,\n",
    "                             epochs=epochs, validation_data=valid_generator,\n",
    "                             validation_steps=valid_num//batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curves(history, label, epochs, min_value, max_value):\n",
    "    data = {}\n",
    "    data[label] = history.history[label]\n",
    "    data['val_'+label] = history.history['val_' + label]\n",
    "    \n",
    "    pd.DataFrame(data).plot(figsize = (8,5))\n",
    "    plt.grid(True)\n",
    "    plt.axis([0, epochs, min_value, max_value])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history, 'accuracy', epochs, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history, 'loss', epochs, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_single_img(path):\n",
    "    # read the img through file path\n",
    "    image = tf.io.read_file(path)  \n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    # 原始图片大小为(128, 128, 3)，重设为(64, 64)\n",
    "    image = tf.image.resize(image, [64, 64])  \n",
    "    image = tf.cast(image, tf.float32) / 255.0  # 归一化到[0,1]范围\n",
    "    image = np.expand_dims(image, axis = 0) # since you have batch_size, so you need to expand your image\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_single_pic(path, show=False):\n",
    "    \n",
    "    if show:\n",
    "        import matplotlib.image as mpimg\n",
    "        plt.imshow(mpimg.imread(path))\n",
    "    image = load_and_preprocess_single_img(path)\n",
    "    predict_result = model.predict(image)\n",
    "    print(\"This is\", label_names[np.argmax(predict_result, axis=1)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Leona\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pic_path = \"./lol_data_1/test_2/13.png\"\n",
    "evaluate_single_pic(test_pic_path, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Caitlyn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pic_path2 = \"./lol_data_1/test_2/2.png\"\n",
    "evaluate_single_pic(test_pic_path2, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow-GPU",
   "language": "python",
   "name": "tf2_gpu"
  },
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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